Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems

• Published in: Journal of Building Engineering Vol. 66; p. 105830
• Main Author: Li, Ning
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Air conditioning system; Artificial neural network; Control characteristics; Genetic algorithm; ANN; GA; Air conditioning system; Control characteristics; Artificial neural network; Genetic algorithm
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2023.105830

Air conditioning systems are playing an increasingly important role in our daily life, and the control of air conditioning systems is related to the intelligence of the system, indoor thermal comfort and energy consumption. Therefore, the control of air conditioning systems has always been an important research topic. Artificial neural network (ANN) and genetic algorithm (GA) are two commonly used intelligent control methods. However, there is no guidance until now on the applicability of these two control methods and how to make a choice between them in the control of air conditioning systems. In this study, an ANN control strategy and a GA control strategy were developed and experimentally verified. The experimental results indicated that both the ANN control strategy and the GA control strategy could control the air conditioning system properly under different control commands. To compare the control performances, the convergence speed, discrete characteristic, energy consumption and interference resistance ability were calculated or experimentally validated. The ANN control strategy showed better performances in the convergence speed and energy consumption. While the GA control strategy performed better in maintaining the stable state of the air conditioning system. The innovation of this study lies in two points. First, when designing the ANN control strategy and the GA control strategy, ANN and GA were applied as the central control algorithm, rather than only as auxiliary algorithms for system identification, prediction or optimization. This methodology is relatively novel in the design of ANN and GA control strategies for the air conditioning system, and could further expand the application of ANN and GA. Second, this study employed 4 evaluating indicators including convergence speed, discrete characteristic, energy consumption and interference resistance ability, to comprehensively evaluated the control performance of ANN control strategy and GA control strategy. •We develop two control strategies using ANN and GA.•The control strategies were validated experimentally.•The control performances were assessed and compared.•Four evaluating indicators were employed.